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(Re)introduction to Mx: Data Preparation and Analysis Guide

Learn how to prepare and analyze data using Mx software for genetic modeling. Understand matrix building blocks, constraint matrices, shortcuts, setting up scripts, testing for significance, and reporting results.

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(Re)introduction to Mx: Data Preparation and Analysis Guide

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  1. (Re)introduction to MxSarah Medland

  2. Kiwi Chinese Gooseberry

  3. Starting at the beginning • Data preparation • Mx expects 1 line per case/family • Almost limitless number of families and variables • Space delimited is best • Can use a missing code ie -9 or can use the default ‘.’

  4. Important structural stuff • Script is composed of one or more jobs (can handle many ‘nested’ jobs in one script or 2 non nested jobs) • Each job is composed of one or more groups • Each group is ‘opened’ with a title • Each group is ‘closed’ with an end statement • You must tell Mx how many groups will be in the job

  5. A bit about groups • 3 types of groups • Calculation • Data • If analysing raw data Mx expects a Means Model and a Covariance Model • Constraint

  6. Matrices: the building blocks • Many types • Denoted by a single letter • Elements defined by letter and 3 numbers • A 1 2 1 = A matrix group 1 row 2 column 1 • All constants and estimated parameters must be placed in a matrix & Mx must be told what type of matrix it is • Letters can be reused in subsequent groups

  7. Matrices: the building blocks • Manytypes

  8. Short cuts • Anything after ! is read as a comment • Can predefine frequently used/changed parameters • #define nvar2=2 • Can read in another file within the script • #include ozbmi2.dat • Can run loops – via the repeat comand • Use an end of line signal (; or /) except in the Labels command

  9. Setting up the script – calculation group • 1st line is the title • 2nd specifies group type • Matrix definition • Begin Matrices – End Matrices • If a matrix is not specified free it will be considered fixed • Algebra • Begin Algebra – End Algebra • Starting values for free/estimated parameters or specified values for constants • End

  10. Setting up the script – data group • 1st line is the title • 2nd specifies group type and number of variables (in ozbmi2.dat) • 3rd line gives data location (in ozbmi2.dat) • Rectangular file = continuous data • Ordinal file = ordinal data (Mx will expect a thresholds model not a means model) • List the variables (in ozbmi2.dat) • Select if … • Select variables • Order is important! Select all vs for twin1 then twin2 then sib1 ect • Specify which vs are covariates (definition variables)

  11. Setting up the script – data group • Matrix definition • Call matrices from previous groups and/or define new matrices • Algebra & starting values • Means Model • can include covariates ie age, sex … • Covariance Model • Expected to be nsib*nvar by nsib*nvar • End

  12. 1/0.5 1 C E A A C E Variance/covariance matrices MZ t1 t2 t1 a2+c2+e2 a2+c2 t2 a2+c2 a2+c2+e2 DZ t1 t2 t1 a2+c2+e2 0.5a2+c2 t2 0.5a2+c2 a2+c2+e2 z x y x y z P-t1 P-t2

  13. So what do you get • Mx starts by reading back the script

  14. So what do you get • Data summary

  15. So what do you get • Parameter specifications

  16. So what do you get • Estimates

  17. So what do you get • Warnings & Fit information (not from ozbmiyface.mx)

  18. Testing for significance • Drop the parameter(s) from the model or equate parameters using the multiple job option • Specify the matrix elements you wish to drop/equate • Drop A 1 1 1 • EQ A 1 1 1 B 1 1 1 • Compare the fit of the two models

  19. 1/0.5 1 C E A A C E Variance/covariance matrices MZ t1 t2 t1 a2+c2+e2 a2+c2 t2 a2+c2 a2+c2+e2 DZ t1 t2 t1 a2+c2+e2 0.5a2+c2 t2 0.5a2+c2 a2+c2+e2 z x y x y z P-t1 P-t2

  20. What to report • Summary statistics • Usually from a simplified ‘saturated’ model • Standardized estimates • Easier to conceptualise • ie 40% of the phenotypic variance vs a genetic effect of 2.84 • Can easily be returned to original scale if summary statistics are provided

  21. What to report • Path coefficients • Very Important in multivariate analyses • Gives a much clearer picture of the directionality of effects • Variance components/proportion of variance explained • Genetic correlations

  22. General Advice/Problem solving • Scripting styles differ • Check the parameter numbers • Check the sample description • Learn to love the manual • Comments are your friends

  23. Time for coffee explodingdog.com

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